Instructions to use mradermacher/MiniMax-M2.1-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/MiniMax-M2.1-i1-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/MiniMax-M2.1-i1-GGUF", dtype="auto") - llama-cpp-python
How to use mradermacher/MiniMax-M2.1-i1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mradermacher/MiniMax-M2.1-i1-GGUF", filename="MiniMax-M2.1.i1-IQ1_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/MiniMax-M2.1-i1-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf mradermacher/MiniMax-M2.1-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/MiniMax-M2.1-i1-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mradermacher/MiniMax-M2.1-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/MiniMax-M2.1-i1-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf mradermacher/MiniMax-M2.1-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/MiniMax-M2.1-i1-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf mradermacher/MiniMax-M2.1-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/MiniMax-M2.1-i1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/MiniMax-M2.1-i1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/MiniMax-M2.1-i1-GGUF with Ollama:
ollama run hf.co/mradermacher/MiniMax-M2.1-i1-GGUF:Q4_K_M
- Unsloth Studio
How to use mradermacher/MiniMax-M2.1-i1-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mradermacher/MiniMax-M2.1-i1-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mradermacher/MiniMax-M2.1-i1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mradermacher/MiniMax-M2.1-i1-GGUF to start chatting
- Pi
How to use mradermacher/MiniMax-M2.1-i1-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mradermacher/MiniMax-M2.1-i1-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "mradermacher/MiniMax-M2.1-i1-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mradermacher/MiniMax-M2.1-i1-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mradermacher/MiniMax-M2.1-i1-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default mradermacher/MiniMax-M2.1-i1-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use mradermacher/MiniMax-M2.1-i1-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mradermacher/MiniMax-M2.1-i1-GGUF:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "mradermacher/MiniMax-M2.1-i1-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use mradermacher/MiniMax-M2.1-i1-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/MiniMax-M2.1-i1-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/MiniMax-M2.1-i1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/MiniMax-M2.1-i1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.MiniMax-M2.1-i1-GGUF-Q4_K_M
List all available models
lemonade list
Thanks mradermacher!
Just wanted to share my appreciation for your quants!
I don't know what's going on exactly, but I get noticably faster token generation (30t/s on Ryzen AI 395 vs 25t/s) out of your IQ3 quant compared to other Q2_* quants despite your iquant having more bits per weight.
This is my new go-to model for reasoning and coding :)
I don't know what's going on exactly, but I get noticably faster token generation (30t/s on Ryzen AI 395 vs 25t/s) out of your IQ3 quant compared to other Q2_* quants despite your iquant having more bits per weight.
Hi @wimmmm , it's really interesting to read about the specific speed of larger models for the Ryzen AI 395.
What would always be interesting and valuable information (and which can't be found anywhere!) is how fast larger models run on the Ryzen AI 395.
Do you have experience with models such as GLM-4.7 Q5_K_M (255GB) or IQ4_XS (192GB) or similar (Deepseek) how fast they run on a Ryzen AI 395 ??
Such large models on limited systems must always run in hybrid mode partially from SSD... (ik_llama and settings for -ngl and -ot exps=CPU are particularly interesting here)